If you're just getting started on your machine learning (ML) journey, you've probably come across terms like pipeline, workflow, and lifecycle. I’m pretty methodical when it comes to picking up new tech, so I prefer to get a handle on these definitions sooner rather than later.
The terms ML Pipeline, ML Lifecycle, and ML Workflow are often used interchangeably, but they refer to different aspects of the machine learning development and deployment process. Here’s a breakdown of the differences:
ML Pipeline
An ML Pipeline refers to a specific, sequential series of steps or processes involved in training and deploying a machine learning model. It represents the structured flow from raw data to a deployed model.
Key Characteristics:
- Linear and Sequential: Typically, an ML pipeline follows a linear path where the output of one step is the input for the next.
- Components: Common components of an ML pipeline include data collection, data preprocessing, feature engineering, model training, model evaluation, and model deployment.
- Automation: Pipelines are often automated, allowing repetitive tasks (like retraining a model) to be performed consistently and efficiently.
Example: A typical ML pipeline might involve the following steps:
- Data Ingestion
- Data Preprocessing (e.g. cleaning, normalization)
- Feature Engineering
- Model Training
- Model Evaluation
- Model Deployment
Tools like TensorFlow Extended (TFX), Apache Airflow, KubeFlow Pipelines, and SageMaker Pipelines are primarily used to manage the sequential steps from data preprocessing to model deployment.
ML Lifecycle
The ML Lifecycle encompasses the entire process of developing and maintaining a machine learning model, from the initial concept to the model’s retirement. It’s a broader term that includes multiple iterations and the ongoing management of models.
Key Characteristics:
- End-to-End Process: The ML lifecycle covers everything from defining the problem and collecting data to deploying the model and monitoring its performance over time.
- Iterative: Unlike a pipeline, which is often linear, the ML lifecycle is iterative. Models may be retrained, improved, and redeployed as new data becomes available or as performance degrades.
- Lifecycle Stages: Common stages include problem definition, data collection, model development, model deployment, monitoring, and model retirement or replacement.
Example: A full ML lifecycle might look like this:
- Problem Definition and Hypothesis
- Data Collection and Exploration
- Model Development (including pipeline creation)
- Model Deployment
- Continuous Monitoring and Maintenance
- Model Update or Replacement
- Model Decommissioning
Tools such as MLFlow, KubeFlow, Azure ML, and DataRobot manage the broader, end-to-end process of developing, deploying, and maintaining ML models.
ML Workflow
An ML Workflow refers to the process or series of tasks that need to be completed to accomplish a specific ML-related task or project. It’s a more flexible and general term that can refer to both high-level processes and detailed tasks within the ML process.
Key Characteristics:
- Task-Oriented: An ML workflow is focused on the sequence and coordination of tasks needed to complete an ML project.
- Flexible: Unlike a pipeline, which is usually rigid, an ML workflow can include loops, branching, and parallel tasks. It’s often represented as a directed acyclic graph (DAG) rather than a straight line.
- Scope: Workflows can vary in scope—from small, specific tasks within an ML pipeline (e.g., hyperparameter tuning) to broader processes that span multiple stages of the ML lifecycle.
Example: An ML workflow might include:
- Data Preprocessing Workflow
- Feature Selection Workflow
- Model Training Workflow
- Model Validation Workflow
- Deployment Workflow
Tools like Apache Airflow, Luigi, Dagster, and Argo Workflows are designed to orchestrate the tasks and processes involved in an ML project, offering flexibility for non-linear and complex workflows.
Summary of Differences
- ML Pipeline: A linear, structured sequence of steps to take data from raw form to a deployed model. It’s focused on automating the process of creating and deploying an ML model.
- ML Lifecycle: The broad, end-to-end process covering the entire journey of an ML model, from initial conception to decommissioning. It includes multiple iterations and cycles of development, deployment, and monitoring.
- ML Workflow: A general term for the tasks and processes involved in an ML project. It can be flexible, encompassing both linear and non-linear sequences of tasks, and can represent anything from a single step in the pipeline to a full project plan.
Each term serves a different purpose in describing the stages and processes involved in creating, deploying, and maintaining machine learning models.